The discipline of genetic epidemiology has had much success in elucidating the etiologies of simple Mendelian disorders and locating the genes involved. Interest has now turned to more complex phenotypes, involving multiple genetic and environmental factors and further complicated by the possibility of interactions between these factors. While a variety of methods for dealing with complex phenotypes have already been introduced, still further methodological advances are necessary to take full advantage of the information contained in multivariate data sets and to explore all the possible genetic and environmental interactions. The purpose of this project is to assess the power and performance of variance component linkage methods to deal with multivariate data, oligogenic linkage, epistasis and pleiotropy. These methods will be evaluated on simulated data and then used to explore the genetic architecture of two complex multivariate data sets, one randomly selected and one ascertained through an affected proband. This project will provide valuable information about the use of variance component linkage methods for complex phenotypes and will generate information regarding the genetics of insulin like growth factors and brain wave patterning.